Online Library

We cannot truly understand terrorism in the 21st century—let alone counter it effectively—unless we also understand the processes of communication that underpin it. The book challenges existing terrorism research showing that current approaches are inadequate and outdated. It exposes the fact that, although we live in an age of interconnectedness shaped by media technologies, both policy makers and security experts know very little about how to make sense of this reality. Among the widespread myths the book dispels are: the idea that new recruits into the ranks of al Qaeda are ‘radicalized’ by a ‘narrative of grievance’; that the removal of extremist websites should be a priority; that ‘we’ can ‘rewrite’ terrorists’ propaganda; that being a ‘global brand’ is a source of strength for al Qaeda. This book will be of interest to researchers and students in terrorism studies, communication and media, politics and security.

This article seeks to investigate the way in which social media can affect terrorist events. Using the 2014 Sydney siege as its primary focus, it will argue that the public’s social media activity, particularly the capacity to engage in ‘reporting’ of live events as they occur, can shift the tactical advantage from counterterrorism officials to the perpetrator. Situational awareness theory will be used to analyse how the public’s Twitter activity during the event had the capacity to enhance the perpetrator’s decision-making and therefore his overall capacity to execute the attack. The article will analyse the Martin Place Siege Joint Commonwealth—New South Wales Review, particularly, Chapter 10, Public Communication. The Review had shortcomings in terms of its failure to fully analyse the role of social media during the Sydney siege and the way in which it impacted upon events. The article therefore seeks to highlight the need for law enforcement and government agencies to take into account developments within social media, which have added a new dimension to terrorist activity. Failure to take account of these developments will diminish the capacity of law enforcement and government to respond effectively to similar events in the future.

Social media messages often provide insights into offline behaviors. Although hate speech proliferates rapidly across social media platforms, it is rarely recognized as a cybercrime, even when it may be linked to offline hate crimes that typically involve physical violence. This paper aims to anticipate violent acts by analyzing online hate speech (hatred, toxicity, and sentiment) and comparing it to offline hate crime. The dataset for this preregistered study included social media posts from X (previously called Twitter) and Facebook and internal police records of hate crimes reported in Spain between 2016 and 2018. After conducting preliminary data analysis to check the moderate temporal correlation, we used time series analysis to develop computational models (VAR, GLMNet, and XGBTree) to predict four time periods of these rare events on a daily and weekly basis. Forty-eight models were run to forecast two types of offline hate crimes, those against migrants and those against the LGBT community. The best model for migrant crime achieved an R² of 64%, while that for LGBT crime reached 53%. According to the best ML models, the weekly aggregations outperformed the daily aggregations, the national models outperformed those geolocated in Madrid, and those about migration were more effective than those about LGBT people. Moreover, toxic language outperformed hatred and sentiment analysis, Facebook posts were better predictors than tweets, and in most cases, speech temporally preceded crime. Although we do not make any claims about causation, we conclude that online inflammatory language could be a leading indicator for detecting potential hate crimes acts and that these models can have practical applications for preventing these crimes.

VOX-Pol Blog Post.

Operations involving the capture, processing, and transmission of terrorist events, campaigns, or images produce effects well beyond the representational/informational functions of media. This article examines several unspoken effects involved in the mediation of terrorism. We analyze the extent to which several mechanisms and operations of western media may be complicit in, if not fundamental to, the global production and administration of terror, particularly at the level of its image and what we call virality. We theorize the ways in which media not only “mediate” terror, but also function to regulate and/or administer it and, in particular, to exacerbate, amplify, and proliferate images and activities of Islamic State in Iraq and Syria (ISIS) across global networks of digital exchange. We argue that key to understanding the strategies and circulating effects of ISIS’s media involvement is the tendency of viral media operations to overproduce, overextend, and oversaturate. The condition of oversaturation denotes a hyperactive global media circuitry that is collapsing under its own weight. This condition reflects a strategic tendency of terror, which underlies all mediatic processing of images deployed by ISIS. It also reveals a vulnerability for terrorist strategy to exacerbate and exhaust the hyperactivity of media, and thus to accelerate the implosive collapse of the globally networked system. We theorize how implicit and unintended effects or outputs of the mediatic processing of terrorist meanings, images, and discourses may work to overstimulate the global system to the point of its reversal, exhaustion, or implosion.

Online discussion forums provide open workspace allowing users to share information, exchange ideas, address problems, and form groups. These forums feature multimodal posts and analyzing them requires a framework that can integrate heterogeneous information extracted from the posts, i.e. text, visual content and the information about user interactions with the online platform and each other. In this paper, we develop a generic framework that can be trained to identify communication behavior and patterns in relation to an entity of interest, be it user, image or text in internet forums. As the case study we use the analysis of violent online political extremism content, which has been a major challenge for domain experts. We demonstrate the generalizability and flexibility of our framework in predicting relational information between multimodal entities by conducting extensive experimentation around four practical use cases.